Probability and Health Equity: Preventive Care Access Vocabulary Review

Vocabulary Review Sheet

Lesson – Probability and Health Equity: Preventive Care Access

How to Use

• Review each word and example before your quiz.
• Connect math definitions to real-world health fairness issues.
• Keep this sheet in your Equity in Numbers Student Journal.
• Remember: Probability is more than numbers — it’s a tool to see who has access and why.

Probability

• Definition: The chance that an event will happen, written as a fraction, decimal, or percent.
• Math Example: P(Visit) = 750 / 1500 = 0.5 → 50%.
• Real-Life Example: Half of all patients in a clinic received a wellness visit.
• Fairness Example: Probability shows who gets access to care and who does not.

Conditional Probability

• Definition: The chance of one event happening given that another has already occurred.
• Math Example: P(Visit | A) = 300 / 500 = 0.60 → 60%.
• Real-Life Example: 60% of patients in Group A had a checkup this year.
• Fairness Example: Comparing conditional probabilities helps us see whether care is equally accessible for each group.

Marginal Probability

• Definition: The overall probability of a single event, ignoring categories.
• Math Example: P(Visit) = 750 / 1500 = 0.5.
• Real-Life Example: Half of all surveyed people went to the doctor this year.
• Fairness Example: Looking only at the marginal rate can hide gaps between groups.

Joint Probability

• Definition: The chance that two events happen together.
• Math Example: P(Group A and Visit) = 300 / 1500 = 0.20 → 20%.
• Real-Life Example: 20% of all people in the dataset were from Group A who visited the clinic.
• Fairness Example: Joint probability helps show how different communities fit into overall health patterns.

Independence

• Definition: Two events are independent if one does not affect the probability of the other.
• Math Example: If P(Visit | Group) = P(Visit) for all groups, then events are independent.
• Real-Life Example: Health visit rates don’t change based on group membership.
• Fairness Example: If events are not independent, it suggests systemic differences in access.

Two-Way Table

• Definition: A table that shows frequencies for two categories of data.
• Math Example: Rows = Groups; Columns = Visit vs No Visit.
• Real-Life Example: Clinic data tracking who attended a checkup by group.
• Fairness Example: Two-way tables help visually compare access across populations.

Relative Risk

• Definition: A ratio comparing one group’s probability to another’s.
• Formula: Relative Risk = (lower rate ÷ higher rate).
• Math Example: 0.40 ÷ 0.60 = 0.67 → Group C is 67% as likely to visit as Group A.
• Real-Life Example: Shows how one group’s chance of receiving care compares to another’s.
• Fairness Example: Relative risk reveals disparities that need policy attention.

Gap (Percentage-Point Difference)

• Definition: The difference between two rates in percentage points.
• Math Example: 60% – 40% = 20 points.
• Real-Life Example: Group C has a 20-point lower visit rate than Group A.
• Fairness Example: Every gap represents a barrier — closing it means more equity in care.

Complementary Events

• Definition: Two events that make up the whole set; their probabilities add to 1.
• Math Example: P(Visit) + P(No Visit) = 1.
• Real-Life Example: A patient either receives a checkup or does not.
• Fairness Example: Focusing on the “no-visit” side shows where support is most needed.

Intersection (∩)

• Definition: Where two events happen together.
• Math Example: P(A ∩ Visit) = 0.20.
• Real-Life Example: Patients who belong to Group A and had a checkup.
• Fairness Example: Identifying which communities are most served helps balance resources.

Union (∪)

• Definition: When either one or both events occur.
• Math Example: P(A ∪ Visit) = P(A) + P(Visit) – P(A ∩ Visit).
• Real-Life Example: Anyone who is in Group A or who visited the clinic (or both).
• Fairness Example: Helps estimate the total reach of a health program across groups.

Interpretation

• Definition: Explaining what probabilities mean in real terms.
• Math Example: “Group C’s P(Visit | C) = 0.40 means only 4 of 10 people got checkups.”
• Real-Life Example: Turning numbers into stories of access and barriers.
• Fairness Example: Interpretation lets data speak for communities and guide solutions.

Summary of Math + Fairness Connections

Concept	Math Focus	Fairness Connection
Conditional Probability	P(event	group)
Independence	Compare rates across groups	Equal rates = equal opportunity
Relative Risk	Ratio of probabilities	Measures strength of inequity
Gap Analysis	% difference between groups	Identifies barriers to care
Interpretation	Explain results in context	Turns math into advocacy for equity